However, in practice, classifying an image with a single label is not enough, and more information is desirable as how many defects are there in an radiography. This is the case for object detection problems where the objective is to define bounding boxes pointing to an object plus giving a label to such defect (a classification problem). Hence, object detection is a more difficult task not only because of the bounding box location, suppression of repeated bounding boxes etc., but also the detection of small objects or objects subject to occlusion, where some important features of the object may be not visible but we still want to detect it. Using this approach, Fergusson et al. [1]} proposed a Mask Region based Convolutional Neural Network (CNN) that could be used to simultaneously perform defect detection and pixel segmentation based on GDXRay castings and welding database (GRIMA - Group of Machine Intelligence, Mery et al. [2]}). The Mask R-CNN model [3]} was based on a ResNet-101 backbone [4]} to create region proposals and on a Faster R-CNN model [5]} for the Region Based Detector (RBD) that finally classifies the casting defects and identified the associated bounding box coordinates. By using only the object detection part of the model, Ferguson achieved 0.931 mAP but could be increased up to 0.957 mAP using segmentation techniques which involved a complex and time-consuming process of creation of the exact pixels that define the Ground Truth (GT) defect.
